Welcome
User Guides & Documentation
Map
View detected dumpsites on map, add ignore zones
Open Layers, Layer switcher, change base map, view terrain, adjust color and opacity
Direct GeoTiff Injection, review historical image layers of areas of interest
Map Configurator
Configure the map to fit your needs
Customize layers, base maps and more ...
Further Development: GeoServer Integration ...
Dataset Management
Manage extensive datasets of drone and satellite images
Tools for uploading, categorizing, and maintaining image data
Features include tagging, filtering, and robust data integrity checks
Image Annotation
Annotate high-resolution drone and satellite imagery
Draw, adjust and enable/disable bounding boxes
Further Development: Segmentation polygon annotation
AI Model Training
Train proven AI model architectures with your custom datasets
Reinforce your custom-trained models as more data comes in
Detection Input Images
Input images on which detection will be run
Load input images as Map Layers
Waste Detection and Monitoring of Dumpsites
Detect using custom-trained models
Visualize results on georeferenced maps
Landfill Management
Advanced tools for legal landfill management
Waste form submission integration
Application Settings
Save and Configure your own application settings
Run the App Your Way
Localization
Add Localization for your App
Support as many languages as you want
Getting Started
We provide a guide that will help you set up and run the [ Raven Scan ] platform on your local development environment.
The complete installation guide can be found on our official documentation page
Development
Support & Issues
- Check the detailed documentation for troubleshooting
- Create an issue in the GitHub repository
If you are ready to contribute, experiencing a bug, or just curious
Check out the Development Documentation
Licensing
This project is licensed under the Apache License 2.0.
This license allows for a great deal of freedom in both academic and commercial use of this software.
- See the Complete License Text.
Open-Sourced
Collected Dataset
Trained Models
Acknowledgments
We would like to extend our deepest gratitude to the following organizations and platforms for their invaluable support
UNICEF Venture Fund
We express our profound gratitude to the UNICEF Venture Fund for their generous support of our project. Their commitment to fostering innovation and sponsoring projects that utilize frontier technology is truly commendable and instrumental in driving positive change.
MMDetection
A special thanks to the open-source AI training platform MMDetection. Your robust tools and extensive resources have significantly accelerated our development process.
Third Party Notices
Our project would not have been possible without the myriad of libraries and frameworks that have empowered us along the way. We owe a great debt of gratitude to all the contributors and maintainers of these projects.
Thank you to everyone who has made this project possible. We couldn't have done it without you!
Raven Scan uses third-party libraries or other resources that may be distributed under licenses different than the Raven Scan software.
In the event that we accidentally failed to list a required notice, please bring it to our attention by posting an issue on out GitHub Page.
Each team member has played a pivotal role in bringing this project to fruition, and we are immensely thankful for their hard work and dedication.
Code of Conduct
We are committed to fostering a welcoming and inclusive community.
Our project adheres to a Code of Conduct that outlines expectations for participation and community standards for behavior.
We encourage all contributors and participants to review and adhere to these guidelines.
By participating in this project, you agree to abide by its terms.
- Full Code of Conduct
Contributing
We welcome contributions from the community.
Whether you're fixing bugs, adding new features, or improving documentation, your help is greatly appreciated.
For detailed instructions on how to contribute, please see our
- Complete Contributing Guidelines
This documentation is maintained as part of the Raven Scan project.
